// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.

// discard stack allocation as that too bypasses malloc
#define EIGEN_STACK_ALLOCATION_LIMIT 0
// heap allocation will raise an assert if enabled at runtime
#define EIGEN_RUNTIME_NO_MALLOC

#include "main.h"
#include <Eigen/Cholesky>
#include <Eigen/Eigenvalues>
#include <Eigen/LU>
#include <Eigen/QR>
#include <Eigen/SVD>

template<typename MatrixType>
void
nomalloc(const MatrixType& m)
{
	/* this test check no dynamic memory allocation are issued with fixed-size matrices
	 */
	typedef typename MatrixType::Scalar Scalar;

	Index rows = m.rows();
	Index cols = m.cols();

	MatrixType m1 = MatrixType::Random(rows, cols), m2 = MatrixType::Random(rows, cols), m3(rows, cols);

	Scalar s1 = internal::random<Scalar>();

	Index r = internal::random<Index>(0, rows - 1), c = internal::random<Index>(0, cols - 1);

	VERIFY_IS_APPROX((m1 + m2) * s1, s1 * m1 + s1 * m2);
	VERIFY_IS_APPROX((m1 + m2)(r, c), (m1(r, c)) + (m2(r, c)));
	VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0, 0, rows, cols)), (m1.array() * m1.array()).matrix());
	VERIFY_IS_APPROX((m1 * m1.transpose()) * m2, m1 * (m1.transpose() * m2));

	m2.col(0).noalias() = m1 * m1.col(0);
	m2.col(0).noalias() -= m1.adjoint() * m1.col(0);
	m2.col(0).noalias() -= m1 * m1.row(0).adjoint();
	m2.col(0).noalias() -= m1.adjoint() * m1.row(0).adjoint();

	m2.row(0).noalias() = m1.row(0) * m1;
	m2.row(0).noalias() -= m1.row(0) * m1.adjoint();
	m2.row(0).noalias() -= m1.col(0).adjoint() * m1;
	m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint();
	VERIFY_IS_APPROX(m2, m2);

	m2.col(0).noalias() = m1.template triangularView<Upper>() * m1.col(0);
	m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.col(0);
	m2.col(0).noalias() -= m1.template triangularView<Upper>() * m1.row(0).adjoint();
	m2.col(0).noalias() -= m1.adjoint().template triangularView<Upper>() * m1.row(0).adjoint();

	m2.row(0).noalias() = m1.row(0) * m1.template triangularView<Upper>();
	m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template triangularView<Upper>();
	m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template triangularView<Upper>();
	m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template triangularView<Upper>();
	VERIFY_IS_APPROX(m2, m2);

	m2.col(0).noalias() = m1.template selfadjointView<Upper>() * m1.col(0);
	m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.col(0);
	m2.col(0).noalias() -= m1.template selfadjointView<Upper>() * m1.row(0).adjoint();
	m2.col(0).noalias() -= m1.adjoint().template selfadjointView<Upper>() * m1.row(0).adjoint();

	m2.row(0).noalias() = m1.row(0) * m1.template selfadjointView<Upper>();
	m2.row(0).noalias() -= m1.row(0) * m1.adjoint().template selfadjointView<Upper>();
	m2.row(0).noalias() -= m1.col(0).adjoint() * m1.template selfadjointView<Upper>();
	m2.row(0).noalias() -= m1.col(0).adjoint() * m1.adjoint().template selfadjointView<Upper>();
	VERIFY_IS_APPROX(m2, m2);

	m2.template selfadjointView<Lower>().rankUpdate(m1.col(0), -1);
	m2.template selfadjointView<Upper>().rankUpdate(m1.row(0), -1);
	m2.template selfadjointView<Lower>().rankUpdate(m1.col(0), m1.col(0)); // rank-2

	// The following fancy matrix-matrix products are not safe yet regarding static allocation
	m2.template selfadjointView<Lower>().rankUpdate(m1);
	m2 += m2.template triangularView<Upper>() * m1;
	m2.template triangularView<Upper>() = m2 * m2;
	m1 += m1.template selfadjointView<Lower>() * m2;
	VERIFY_IS_APPROX(m2, m2);
}

template<typename Scalar>
void
ctms_decompositions()
{
	const int maxSize = 16;
	const int size = 12;

	typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, 0, maxSize, maxSize> Matrix;

	typedef Eigen::Matrix<Scalar, Eigen::Dynamic, 1, 0, maxSize, 1> Vector;

	typedef Eigen::Matrix<std::complex<Scalar>, Eigen::Dynamic, Eigen::Dynamic, 0, maxSize, maxSize> ComplexMatrix;

	const Matrix A(Matrix::Random(size, size)), B(Matrix::Random(size, size));
	Matrix X(size, size);
	const ComplexMatrix complexA(ComplexMatrix::Random(size, size));
	const Matrix saA = A.adjoint() * A;
	const Vector b(Vector::Random(size));
	Vector x(size);

	// Cholesky module
	Eigen::LLT<Matrix> LLT;
	LLT.compute(A);
	X = LLT.solve(B);
	x = LLT.solve(b);
	Eigen::LDLT<Matrix> LDLT;
	LDLT.compute(A);
	X = LDLT.solve(B);
	x = LDLT.solve(b);

	// Eigenvalues module
	Eigen::HessenbergDecomposition<ComplexMatrix> hessDecomp;
	hessDecomp.compute(complexA);
	Eigen::ComplexSchur<ComplexMatrix> cSchur(size);
	cSchur.compute(complexA);
	Eigen::ComplexEigenSolver<ComplexMatrix> cEigSolver;
	cEigSolver.compute(complexA);
	Eigen::EigenSolver<Matrix> eigSolver;
	eigSolver.compute(A);
	Eigen::SelfAdjointEigenSolver<Matrix> saEigSolver(size);
	saEigSolver.compute(saA);
	Eigen::Tridiagonalization<Matrix> tridiag;
	tridiag.compute(saA);

	// LU module
	Eigen::PartialPivLU<Matrix> ppLU;
	ppLU.compute(A);
	X = ppLU.solve(B);
	x = ppLU.solve(b);
	Eigen::FullPivLU<Matrix> fpLU;
	fpLU.compute(A);
	X = fpLU.solve(B);
	x = fpLU.solve(b);

	// QR module
	Eigen::HouseholderQR<Matrix> hQR;
	hQR.compute(A);
	X = hQR.solve(B);
	x = hQR.solve(b);
	Eigen::ColPivHouseholderQR<Matrix> cpQR;
	cpQR.compute(A);
	X = cpQR.solve(B);
	x = cpQR.solve(b);
	Eigen::FullPivHouseholderQR<Matrix> fpQR;
	fpQR.compute(A);
	// FIXME X = fpQR.solve(B);
	x = fpQR.solve(b);

	// SVD module
	Eigen::JacobiSVD<Matrix> jSVD;
	jSVD.compute(A, ComputeFullU | ComputeFullV);
}

void
test_zerosized()
{
	// default constructors:
	Eigen::MatrixXd A;
	Eigen::VectorXd v;
	// explicit zero-sized:
	Eigen::ArrayXXd A0(0, 0);
	Eigen::ArrayXd v0(0);

	// assigning empty objects to each other:
	A = A0;
	v = v0;
}

template<typename MatrixType>
void
test_reference(const MatrixType& m)
{
	typedef typename MatrixType::Scalar Scalar;
	enum
	{
		Flag = MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor
	};
	enum
	{
		TransposeFlag = !MatrixType::IsRowMajor ? Eigen::RowMajor : Eigen::ColMajor
	};
	Index rows = m.rows(), cols = m.cols();
	typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, Flag> MatrixX;
	typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic, TransposeFlag> MatrixXT;
	// Dynamic reference:
	typedef Eigen::Ref<const MatrixX> Ref;
	typedef Eigen::Ref<const MatrixXT> RefT;

	Ref r1(m);
	Ref r2(m.block(rows / 3, cols / 4, rows / 2, cols / 2));
	RefT r3(m.transpose());
	RefT r4(m.topLeftCorner(rows / 2, cols / 2).transpose());

	VERIFY_RAISES_ASSERT(RefT r5(m));
	VERIFY_RAISES_ASSERT(Ref r6(m.transpose()));
	VERIFY_RAISES_ASSERT(Ref r7(Scalar(2) * m));

	// Copy constructors shall also never malloc
	Ref r8 = r1;
	RefT r9 = r3;

	// Initializing from a compatible Ref shall also never malloc
	Eigen::Ref<const MatrixX, Unaligned, Stride<Dynamic, Dynamic>> r10 = r8, r11 = m;

	// Initializing from an incompatible Ref will malloc:
	typedef Eigen::Ref<const MatrixX, Aligned> RefAligned;
	VERIFY_RAISES_ASSERT(RefAligned r12 = r10);
	VERIFY_RAISES_ASSERT(Ref r13 = r10); // r10 has more dynamic strides
}

EIGEN_DECLARE_TEST(nomalloc)
{
	// create some dynamic objects
	Eigen::MatrixXd M1 = MatrixXd::Random(3, 3);
	Ref<const MatrixXd> R1 = 2.0 * M1; // Ref requires temporary

	// from here on prohibit malloc:
	Eigen::internal::set_is_malloc_allowed(false);

	// check that our operator new is indeed called:
	VERIFY_RAISES_ASSERT(MatrixXd dummy(MatrixXd::Random(3, 3)));
	CALL_SUBTEST_1(nomalloc(Matrix<float, 1, 1>()));
	CALL_SUBTEST_2(nomalloc(Matrix4d()));
	CALL_SUBTEST_3(nomalloc(Matrix<float, 32, 32>()));

	// Check decomposition modules with dynamic matrices that have a known compile-time max size (ctms)
	CALL_SUBTEST_4(ctms_decompositions<float>());

	CALL_SUBTEST_5(test_zerosized());

	CALL_SUBTEST_6(test_reference(Matrix<float, 32, 32>()));
	CALL_SUBTEST_7(test_reference(R1));
	CALL_SUBTEST_8(Ref<MatrixXd> R2 = M1.topRows<2>(); test_reference(R2));
}
